{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"CVPPP.ipynb","provenance":[],"collapsed_sections":[]},"kernelspec":{"name":"python3","display_name":"Python 3"},"accelerator":"GPU"},"cells":[{"cell_type":"code","metadata":{"id":"crKCDeunhAcT","colab_type":"code","outputId":"107917cf-ee88-4e64-973d-67aeb29fc571","executionInfo":{"status":"ok","timestamp":1585051019092,"user_tz":-180,"elapsed":28763,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghll_774vW9BtmBXKN1j4g_KVaV_c3XKJ6hdgusNg=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":122}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"I31EttDQq2YU","colab_type":"code","colab":{}},"source":["import os\n","import os.path\n","import random\n","import numpy as np\n","\n","import torch.utils.data as data\n","from PIL import Image\n","\n","def default_loader(filepath):\n","    return Image.open(filepath).convert('RGB')\n","\n","class Reader(data.Dataset):\n","    def __init__(self, image_list, labels_list=[], edge_list=[], transform=None, target_transform=None, use_cache=True, loader=default_loader):\n","        \n","        self.images = image_list\n","        self.loader = loader\n","        \n","        if len(labels_list) is not 0:\n","            assert len(image_list) == len(labels_list)\n","            self.labels = labels_list\n","            self.edges = edge_list\n","        else:\n","            self.labels = False\n","            self.edges = False\n","\n","        self.transform = transform\n","        self.target_transform = target_transform\n","\n","        self.cache = {}\n","        self.use_cache = use_cache\n","\n","    def __len__(self):\n","        return len(self.images)\n","\n","    def __getitem__(self, idx):\n","        if idx not in self.cache:           \n","            img = self.loader(self.images[idx])\n","            if self.labels:\n","                target = Image.open(self.labels[idx])\n","                edge = Image.open(self.edges[idx])\n","            else:\n","                target = None\n","        else:\n","            img,target,edge = self.cache[idx]\n","            \n","        if self.use_cache:\n","            self.cache[idx] = (img, target, edge)\n","\n","        seed = np.random.randint(2147483647)\n","        random.seed(seed)\n","        \n","        if self.transform is not None:\n","            img = self.transform(img)\n","\n","        random.seed(seed)\n","        if self.labels:\n","            if self.target_transform is not None:\n","                target = self.target_transform(target)\n","\n","        random.seed(seed)\n","        if self.edges:\n","            if self.target_transform is not None:\n","                edge = self.target_transform(edge)\n","            \n","        return np.array(img), np.array(target), np.array(edge)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"n2dZAxjRhOwl","colab_type":"code","colab":{}},"source":["from os import listdir\n","from os.path import join\n","\n","import numpy as np\n","import torch\n","import torch.nn as nn\n","import torch.nn.functional as F\n","from torch import optim\n","from torchvision import transforms\n","from torch.utils.data import DataLoader \n","\n","basepath = 'gdrive/My Drive/Datasets/A1'\n","\n","rgb = sorted([join(basepath, f) for f in listdir(basepath) if f.endswith('_rgb.png')])\n","labels = sorted([join(basepath, f) for f in listdir(basepath) if f.endswith('_label.png')])\n","edges = sorted([join(basepath, f) for f in listdir(basepath) if f.endswith('_edge.png')])\n","\n","if 0 == len(rgb):\n","    print(\"No cvppp dataset found in:\" + basepath)\n","    exit(-1)\n","\n","# Check the names are paired correctly\n","assert np.array([img[:-7] == lbl[:-9] for img, lbl in zip(rgb, labels)]).all() == True\n","\n","\n","np.random.seed(1203412412)\n","indexes = np.random.permutation(len(rgb))\n","perm_rgb = np.array(rgb)[indexes].tolist()\n","perm_labels = np.array(labels)[indexes].tolist()\n","\n","\n","dimx,dimy = 448,448\n","\n","transform = transforms.Compose(\n","    [transforms.RandomHorizontalFlip(),\n","     transforms.RandomVerticalFlip(),\n","     transforms.RandomResizedCrop(448,scale=(0.7,1.)),\n","     transforms.ToTensor(),\n","     transforms.Normalize(mean=[0.485, 0.456, 0.406],\n","                          std=[0.229, 0.224, 0.225])])\n","\n","class ToLogits(object):\n","    def __init__(self,expand_dim=None):\n","        self.expand_dim = expand_dim\n","\n","    def __call__(self, pic):\n","        if pic.mode == 'I':\n","            img = torch.from_numpy(numpy.array(pic, numpy.int32, copy=False))\n","        elif pic.mode == 'I;16':\n","            img = torch.from_numpy(numpy.array(pic, numpy.int32, copy=True))\n","        elif pic.mode == 'F':\n","            img = torch.from_numpy(numpy.array(pic, numpy.float32, copy=False))\n","        elif pic.mode == '1':\n","            img = 255 * torch.from_numpy(numpy.array(pic, numpy.uint8, copy=False))\n","        else:\n","            img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))\n","        if pic.mode == 'YCbCr':\n","            nchannel = 3\n","        elif pic.mode == 'I;16':\n","            nchannel = 1\n","        else:\n","            nchannel = len(pic.mode)\n","        img = img.view(pic.size[1], pic.size[0], nchannel)\n","        img = img.transpose(0, 1).transpose(0, 2).contiguous()\n","        if self.expand_dim is not None:\n","            return img.unsqueeze(self.expand_dim)\n","        return img\n","\n","transform_target = transforms.Compose(\n","    [transforms.RandomHorizontalFlip(),\n","     transforms.RandomVerticalFlip(),\n","     transforms.RandomResizedCrop(448,scale=(0.7,1.),interpolation=0),\n","     ToLogits()])"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"_e-ZKe5O2AFU","colab_type":"code","colab":{}},"source":["def log_weights_norm(gain=1.):\n","    def f(w):\n","        w[w < 2] = 2.\n","        w = gain / torch.log(w)\n","        return w\n","    return f\n","\n","def get_embeddings_fast(numpy_gt, sin_pattern,  weights_norm=None):\n","    nobj = numpy_gt.max().item()+1\n","    nsamples, xdim, ydim = numpy_gt.shape\n","    nemb = sin_pattern.size(0)\n","\n","    t = sin_pattern.transpose(1, 0).transpose(2, 1).view(1, -1, nemb).repeat(nsamples, 1, 1)\n","\n","    indexes_raw = numpy_gt.float()\n","    indexes = numpy_gt.long()\n","    w = torch.zeros(nsamples, nobj).to(sin_pattern.device)\n","    w = w.scatter_add(1, indexes.view(nsamples, -1), torch.ones_like(indexes_raw).view(nsamples, -1))\n","    e = torch.zeros(nsamples, nobj, nemb).to(sin_pattern.device)\n","    e = e.scatter_add(1, indexes.view(nsamples, -1, 1).repeat(1, 1, nemb), t)\n","    w[w == 0] = 1.\n","    e = e / w.unsqueeze(-1)\n","    if weights_norm is not None:\n","        w = weights_norm(w)\n","\n","    w = torch.gather(w, 1, indexes.view(nsamples, -1))\n","    e = torch.gather(e, 1, indexes.view(nsamples, -1, 1).repeat(1, 1, nemb))\n","    e = e.transpose(2, 1).contiguous()\n","\n","    return e.view(nsamples, nemb, xdim, ydim), w.view(nsamples, xdim, ydim)\n","\n","class EmbedderFast:\n","    def __init__(self, sin_pattern, weights_norm=None):\n","        self.sin_pattern = sin_pattern\n","        self.weights_norm = weights_norm\n","\n","    def __call__(self, numpy_gt):\n","        return get_embeddings_fast(numpy_gt, self.sin_pattern, self.weights_norm)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"69oumEGLoV0E","colab_type":"code","colab":{}},"source":["\n","class GatedConv(nn.Module):\n","    def __init__(self, in_dims, out_dims, kernel_size=3, padding=1, stride=1, dilation=1):\n","        super(GatedConv, self).__init__()\n","        self.f = nn.Conv2d(in_dims, out_dims, kernel_size=kernel_size, padding=padding, stride=stride,\n","                           dilation=dilation)\n","        self.g = nn.Conv2d(in_dims, out_dims, kernel_size=kernel_size, padding=padding, stride=stride,\n","                           dilation=dilation)\n","\n","    def forward(self, x):\n","        mask = torch.sigmoid(self.g(x))\n","        return self.f(x) * mask\n","\n","class GuidedConv(nn.Module):\n","  def __init__(self, guides, downscale, in_channels, out_channels, kernel_size, stride=1, padding=0,conv_op= nn.Conv1d):\n","    super(GuidedConv,self).__init__()\n","    self.guides = nn.functional.interpolate(guides.clone().detach().unsqueeze(0),scale_factor=1./downscale,mode='bilinear')\n","    self.guides.requires_grad_(False)\n","    self.conv = nn.Conv2d(in_channels+self.guides.size(1),out_channels,kernel_size,stride,padding)\n","  def forward(self,x):\n","    joimnt = torch.cat([x,self.guides.repeat(x.size(0),1,1,1)],dim=1)\n","    return self.conv(joimnt)\n","\n","class double_conv(nn.Module):\n","    '''(conv => BN => ReLU) * 2'''\n","    def __init__(self, in_ch, out_ch,conv_op=nn.Conv2d):\n","        super(double_conv, self).__init__()\n","        self.conv = nn.Sequential(\n","            conv_op(in_ch, out_ch, 3, padding=1),\n","            nn.BatchNorm2d(out_ch),\n","            nn.ReLU(inplace=True),\n","            conv_op(out_ch, out_ch, 3, padding=1),\n","            nn.BatchNorm2d(out_ch),\n","            nn.ReLU(inplace=True)\n","        )\n","\n","    def forward(self, x):\n","        x = self.conv(x)\n","        return x\n","\n","\n","class down(nn.Module):\n","    def __init__(self, in_ch, out_ch,conv_op=nn.Conv2d):\n","        super(down, self).__init__()\n","        self.mpconv = nn.Sequential(\n","            nn.MaxPool2d(2),\n","            double_conv(in_ch, out_ch,conv_op)\n","        )\n","\n","    def forward(self, x):\n","        x = self.mpconv(x)\n","        return x\n","\n","class ups(nn.Module):\n","    def __init__(self, in_ch, out_ch, guides, downscale=1,conv_op=nn.Conv2d):\n","        super(ups, self).__init__()\n","\n","        #  would be a nice idea if the upsampling could be learned too,\n","        #  but my machine do not have enough memory to handle all those weights\n","        self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n","        self.sinconv = GuidedConv(guides, downscale, in_channels=in_ch, out_channels=out_ch, kernel_size=7, padding=3,conv_op=conv_op)\n","        self.conv = double_conv(out_ch, out_ch,conv_op)\n","\n","    def forward(self, x1, x2):\n","        x1 = self.up(x1)\n","      \n","        diffX = x1.size()[2] - x2.size()[2]\n","        diffY = x1.size()[3] - x2.size()[3]\n","        x2 = F.pad(x2, (diffX // 2, int(diffX / 2),\n","                        diffY // 2, int(diffY / 2)))\n","        x = torch.cat([x2, x1], dim=1)\n","        x = self.sinconv(x)\n","        x = self.conv(x)\n","        return x\n","\n","class out_mod(nn.Module):\n","    '''(conv => BN => ReLU) * 2'''\n","    def __init__(self, in_ch, out_ch,conv_op=nn.Conv2d):\n","      super(out_mod, self).__init__()\n","      self.outc = double_conv(in_ch, in_ch,conv_op)\n","      self.attention = nn.Sequential(conv_op(in_ch,out_ch,3,1,1),\n","                                     nn.Sigmoid())\n","      self.outc2 =conv_op(in_ch,out_ch,3,1,1)\n","      self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)\n","    def forward(self, xr, y=None):\n","      x = self.outc2(self.outc(xr))\n","      if y is not None:\n","        att = self.attention(xr)\n","        x = att*x + (1.-att)*self.up(y)\n","      return x\n","\n","class UNet(nn.Module):\n","    def __init__(self, n_channels, guides, conv_op=nn.Conv2d):\n","        super(UNet, self).__init__()\n","        guides = guides.detach()\n","        guides.requires_grad_(False)\n","        self.inc = double_conv(n_channels, 64)\n","        self.down1 = down(64, 128,conv_op)\n","        self.down2 = down(128, 256,conv_op)\n","        self.down3 = down(256, 512,conv_op)\n","        self.down4 = down(512, 512,conv_op)\n","        self.up1 = ups(1024, 256,guides,8,conv_op)\n","        self.up2 = ups(512, 128,guides,4,conv_op)\n","        self.up3 = ups(256, 64,guides,2,conv_op)\n","        self.up4 = ups(128, 64,guides,1,conv_op)\n","        self.outc4 = out_mod(64, guides.size(0),conv_op)\n","        self.outc3 = out_mod(64, guides.size(0),conv_op)\n","        self.outc2 = out_mod(128, guides.size(0),conv_op)\n","        self.outc1 = out_mod(256, guides.size(0),conv_op)\n","\n","    def forward(self, x):\n","        x1 = self.inc(x)\n","        x2 = self.down1(x1)\n","        x3 = self.down2(x2)\n","        x4 = self.down3(x3)\n","        x5 = self.down4(x4)\n","        x = self.up1(x5, x4)\n","        y = self.outc1(x)\n","        x = self.up2(x, x3)\n","        y = self.outc2(x,y)\n","        x = self.up3(x, x2)\n","        y = self.outc3(x,y)\n","        x = self.up4(x, x1)\n","        return self.outc4(x,y)\n"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"49xsfLfpGPq6","colab_type":"code","colab":{}},"source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","\n","sins = [[-24.22295570373535, 0.0, 0.4452361464500427],\n"," [-14.779047012329102, 0.0, 1.2561423778533936],\n"," [-16.399198532104492, 0.0, -0.3734317719936371],\n"," [18.362571716308594, 0.0, 0.7659217715263367],\n"," [-0.6603534817695618, 0.0, 0.24005144834518433],\n"," [-33.7341423034668, 0.0, -0.4350433051586151],\n"," [0.0, 4.286965370178223, 0.8109257817268372],\n"," [0.0, -19.908288955688477, 0.614355206489563],\n"," [0.0, 22.987812042236328, 0.28104516863822937],\n"," [0.0, 7.108828067779541, 0.4827950894832611],\n"," [0.0, 23.66850471496582, 0.21264752745628357],\n"," [0.0, -22.332250595092773, 1.0007625818252563]]\n","\n","def create_guide_function(alpha,beta,phase,x_dim,y_dim):\n","    alpha = torch.FloatTensor(alpha).unsqueeze(-1).unsqueeze(-1)\n","    beta = torch.FloatTensor(beta).unsqueeze(-1).unsqueeze(-1)\n","    phase= torch.FloatTensor(phase).unsqueeze(-1).unsqueeze(-1)\n","\n","    xx_channel = torch.linspace(0.,1.,x_dim).repeat(1, y_dim, 1).float()\n","    yy_channel = torch.linspace(0.,1.,y_dim).repeat(1, x_dim, 1).transpose(1, 2).float()\n","\n","    xx_channel = xx_channel * alpha\n","    yy_channel = yy_channel * beta\n","    return torch.sin(xx_channel+ yy_channel+phase)\n","\n","sins = np.array(sins)\n","guides = create_guide_function(sins[:,0],sins[:,1],sins[:,2],448,448)\n","#unet = UNet(3,guides.to(device),conv_op=GatedConv).to(device)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"aDiLPkxu4rgz","colab_type":"code","outputId":"c06ef168-c188-4bd2-9dc7-1eb0357d3950","executionInfo":{"status":"ok","timestamp":1585051082782,"user_tz":-180,"elapsed":10031,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghll_774vW9BtmBXKN1j4g_KVaV_c3XKJ6hdgusNg=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":71}},"source":["unet = UNet(3,guides.to(device),conv_op=GatedConv).to(device)"],"execution_count":8,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2506: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n","  \"See the documentation of nn.Upsample for details.\".format(mode))\n"],"name":"stderr"}]},{"cell_type":"code","metadata":{"id":"1hEHkhX1aWde","colab_type":"code","colab":{"base_uri":"https://localhost:8080/","height":34},"outputId":"243b0e0d-4840-45fb-e9a8-816b8863e703","executionInfo":{"status":"ok","timestamp":1585051180016,"user_tz":-180,"elapsed":550,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AOh14Ghll_774vW9BtmBXKN1j4g_KVaV_c3XKJ6hdgusNg=s64","userId":"08747758635891452957"}}},"source":["pytorch_total_params = sum(p.numel() for p in unet.parameters() if p.requires_grad)\n","pytorch_total_params"],"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["43056896"]},"metadata":{"tags":[]},"execution_count":9}]},{"cell_type":"code","metadata":{"id":"TgNN0uv61npz","colab_type":"code","outputId":"9c315fee-3f76-41b3-cfb5-69435c8f9c25","executionInfo":{"status":"error","timestamp":1567169456815,"user_tz":-180,"elapsed":10814876,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["\n","errors = []\n","\n","train_data = Reader(rgb, labels,edges, transform, transform_target)\n","generator = DataLoader(train_data, batch_size=4, shuffle=True, num_workers=8)\n","\n","unet = UNet(3,guides.to(device),conv_op=GatedConv).to(device)\n","optim = torch.optim.Adam(unet.parameters(),lr=1e-5)\n","embedder = EmbedderFast(guides.to(device),weights_norm=log_weights_norm(10.))\n","\n","caption = join(basepath,\"cvppp\")\n","if os.path.exists(caption + '_ckp.t7'):\n","  data = torch.load(caption + '_ckp.t7')\n","  unet.load_state_dict(data['model_state_dict'])\n","  optim.load_state_dict(data['optimizer_state_dict'])\n","\n","for e in range(1000):\n","  for x,y,z in generator:\n","    optim.zero_grad()\n","    y_grad, weight =embedder(y.squeeze().to(device))\n","    y_pred =unet(x.to(device))\n","    z = z.to(device).float()\n","    loss = torch.nn.functional.l1_loss(y_pred,y_grad, reduce=False)\n","    edge_loss = torch.mean(torch.mean(loss, dim=1)*z)\n","    loss = torch.mean((weight.detach() * torch.mean(loss, dim=1)))+10.*edge_loss\n","    loss.backward()\n","    errors+=[loss.item()]\n","    optim.step()\n","      \n","  print(np.log(loss.item()))\n","  torch.save({\n","          'epoch': e,\n","          'model_state_dict': unet.state_dict(),\n","          'optimizer_state_dict': optim.state_dict(),\n","          'loss': loss\n","      }, caption + '_ckp2.t7')\n","\n","plt.plot(errors)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.\n","  \"See the documentation of nn.Upsample for details.\".format(mode))\n","/usr/local/lib/python3.6/dist-packages/torch/nn/_reduction.py:46: UserWarning: size_average and reduce args will be deprecated, please use reduction='none' instead.\n","  warnings.warn(warning.format(ret))\n"],"name":"stderr"},{"output_type":"stream","text":["-3.0772068128236256\n","-3.30778925174196\n","-3.322792634465032\n","-3.1103595378177067\n","-3.350639901587841\n","-3.3983712609650274\n","-3.4328501326381233\n","-3.7157681209100084\n","-3.0467209373616626\n","-3.2642913833646623\n","-3.404698369165618\n","-3.3008044908961596\n","-3.272861353445249\n","-3.412518741447254\n","-3.49937355171241\n","-3.203802388405149\n","-3.3935209937684627\n","-3.2802528124338215\n","-3.037588251375406\n","-3.200714091279856\n","-3.2238353106802444\n","-3.2422778791475753\n","-3.8019818342727403\n","-3.372278845931967\n","-3.1857712508548883\n","-3.495802627438529\n","-3.3533812654762856\n","-3.365953231805793\n","-3.378664505734049\n","-3.4280425002263537\n","-3.5792947362013194\n","-3.22616911625037\n","-3.046954057473092\n","-3.1126639623133605\n","-3.1893300239019786\n","-3.3632595433929637\n","-3.794684994274143\n","-3.4756859296692526\n","-3.5061860806559775\n","-3.1095692533802657\n","-3.2172399209545373\n","-3.21897112606034\n","-3.3436974207609667\n","-2.896869208064237\n","-3.454023545358546\n","-3.248484021022349\n","-3.2659563035457975\n","-3.3298917102233325\n","-3.469856265873294\n","-3.2304441505518953\n","-3.5030581182611353\n","-3.5704036154279843\n","-3.235687325398385\n","-3.379679579917517\n","-3.327736617742225\n","-3.5415374382379694\n","-3.3542981478464915\n","-3.352197235347075\n","-3.3093624664088166\n","-3.019859803811186\n","-3.25123959381545\n","-3.662498049248025\n","-3.1742166722739396\n","-3.654485503056371\n","-3.4210704052666348\n","-3.5023083341762473\n","-3.318478893046245\n","-3.303113177200077\n","-3.42623122759005\n","-3.5777689457277493\n","-3.3096409375195956\n","-3.2447108926855557\n","-3.4011667080922767\n","-3.4515102808001923\n","-2.9374634475269823\n","-3.291379621257587\n","-3.4171040610991588\n","-3.8116477707777605\n","-3.3168241035540134\n","-3.5476210471601104\n","-3.5140467161530826\n","-3.0860905243291334\n","-3.176169358492363\n","-3.2991830078649707\n","-3.189381926812803\n","-3.334115755335425\n","-3.3685885325120366\n","-3.33176740564988\n","-3.304752112321204\n","-3.4560743220060766\n","-3.3095737343527203\n","-3.461916468729406\n","-3.235903290219692\n","-3.5622890871368917\n","-3.060748540680806\n","-3.4098448374262604\n","-3.26559994976442\n","-3.325350969821848\n","-3.3687842369146312\n","-3.290513523830901\n"],"name":"stdout"},{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-7-8ec539cc1723>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     36\u001b[0m       }, caption + '_ckp2.t7')\n\u001b[1;32m     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 38\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"]}]},{"cell_type":"code","metadata":{"id":"gq_Ntly-qG5T","colab_type":"code","outputId":"61c82e69-6bf3-41ab-f046-ac57a7bb9d76","executionInfo":{"status":"ok","timestamp":1567170802574,"user_tz":-180,"elapsed":1204,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":286}},"source":["import matplotlib.pyplot as plt\n","plt.plot(errors)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["[<matplotlib.lines.Line2D at 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y9/2Vo7rc/BBMTW2W/F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\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"UvPrHde8GjdY","colab_type":"code","outputId":"9b3d9f3d-cb70-43d1-8fc9-387dfa7e7e64","executionInfo":{"status":"ok","timestamp":1566985511202,"user_tz":-180,"elapsed":6403,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["caption = join(basepath,\"cvppp\")\n","data = torch.load(caption + '_ckp.t7')\n","unet.load_state_dict(data['model_state_dict'],strict=False)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["IncompatibleKeys(missing_keys=[], unexpected_keys=[])"]},"metadata":{"tags":[]},"execution_count":7}]},{"cell_type":"code","metadata":{"id":"10GHXG0-G-wR","colab_type":"code","colab":{}},"source":["train_data = Reader(rgb, labels, transform, transform_target)"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"oLpGJSNMHY97","colab_type":"code","outputId":"4fd9a563-94aa-4c28-aaf8-92421a232b02","executionInfo":{"status":"ok","timestamp":1567171152120,"user_tz":-180,"elapsed":1101,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["x,y,z = train_data[2]\n","xx = unet(torch.from_numpy(x).unsqueeze(0).to(device))\n","print(x.shape)"],"execution_count":0,"outputs":[{"output_type":"stream","text":["(3, 448, 448)\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"LSuqBjNOn55I","colab_type":"code","outputId":"5aa2c8ad-9818-4ded-c097-01d8226d22f4","executionInfo":{"status":"ok","timestamp":1567170841604,"user_tz":-180,"elapsed":1184,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":304}},"source":["plt.imshow(x.transpose(1,2,0))"],"execution_count":0,"outputs":[{"output_type":"stream","text":["Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n"],"name":"stderr"},{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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c17Aq5l3fiWbTwx+JwR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\nn+F9gs0TNYL5M4yeACzz/Gfh9X3AdWH/yscP/yexyTiBN77Qn0y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3PGjxG6lA6MSeiaIJ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\nKFCFQigUKFCgCoVQKFCgQBX+C5zTkEzjIK0tAAAAAElFTkSuQmCC\n","text/plain":["<Figure size 432x288 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"AP4jhNd0HxV_","colab_type":"code","outputId":"1627450a-490d-4147-ec77-2cf135e4e1f6","executionInfo":{"status":"ok","timestamp":1567171216847,"user_tz":-180,"elapsed":1845,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import matplotlib.pyplot as plt\n","plt.figure(figsize=(20,20))\n","plt.imshow(xx.detach().cpu()[0,1])"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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POWTbPsx4kuLglRNwCrtaT/1hRZ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/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\n","text/plain":["<Figure size 1440x1440 with 1 Axes>"]},"metadata":{"tags":[]}}]},{"cell_type":"code","metadata":{"id":"81r2DHyYJjpB","colab_type":"code","outputId":"bd5f7108-7dab-4d6f-9e96-15f0f92a1e17","executionInfo":{"status":"ok","timestamp":1566893159463,"user_tz":-180,"elapsed":1140,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":34}},"source":["def count(model):\n","    return sum(p.numel() for p in model.parameters() if p.requires_grad)\n","count(unet)"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["43056896"]},"metadata":{"tags":[]},"execution_count":10}]},{"cell_type":"code","metadata":{"id":"ufJOmEhA_8PX","colab_type":"code","outputId":"ad6a1979-0015-4599-de14-90c31e0f1f0a","executionInfo":{"status":"error","timestamp":1566891421652,"user_tz":-180,"elapsed":2163,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":185}},"source":["import matplotlib.pyplot as plt\n","plt.plot(errors)"],"execution_count":0,"outputs":[{"output_type":"error","ename":"NameError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)","\u001b[0;32m<ipython-input-2-04ee55b71e8c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m","\u001b[0;31mNameError\u001b[0m: name 'errors' is not defined"]}]},{"cell_type":"code","metadata":{"id":"_-idIu2Z53a9","colab_type":"code","outputId":"3e6d9a32-53fb-4ee1-959c-2ecaaf1520ad","executionInfo":{"status":"ok","timestamp":1567157262552,"user_tz":-180,"elapsed":37590,"user":{"displayName":"Victor Kulikov","photoUrl":"https://lh3.googleusercontent.com/a-/AAuE7mBCoy3JukrY6MPS5IuiBDGihQcL1h9k8WUB8xedRA=s64","userId":"08747758635891452957"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["import matplotlib.pyplot as plt\n","import cv2\n","\n","for l in labels:\n","  num_array = np.array(Image.open(l)).astype(np.float32)\n","  nap = np.zeros_like(num_array)\n","  dIdx = abs(num_array[:-1,:]-num_array[1: ,:])>0\n","  dIdy = abs(num_array[:,:-1]-num_array[: ,1:])>0\n","  nap[1:]+=dIdx\n","  nap[:,1:]+=dIdy\n","  nap = np.clip(nap,0,1)\n","  nap = cv2.dilate(nap,np.ones((5,5))).astype('uint8')\n","  pil = Image.fromarray(nap)\n","  pil.save(l[:-9]+\"edge.png\",\"PNG\")"],"execution_count":0,"outputs":[{"output_type":"execute_result","data":{"text/plain":["<matplotlib.image.AxesImage at 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fQ9CDKdy1MyjdelJCyY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size 1440x1440 with 1 Axes>"]},"metadata":{"tags":[]}}]}]}